def _from_java_impl(cls, java_stage):
        """
        Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams.
        """

        # Load information from java_stage to the instance.
        estimator = JavaParams._from_java(java_stage.getEstimator())
        evaluator = JavaParams._from_java(java_stage.getEvaluator())
        epms = [estimator._transfer_param_map_from_java(epm)
                for epm in java_stage.getEstimatorParamMaps()]
        return estimator, epms, evaluator

    def _from_java(cls, java_stage):
        """
        Given a Java CrossValidatorModel, create and return a Python wrapper of it.
        Used for ML persistence.
        """
        bestModel = JavaParams._from_java(java_stage.bestModel())
        estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)

        py_stage = cls(bestModel=bestModel).setEstimator(estimator)
        py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)

        if java_stage.hasSubModels():
            py_stage.subModels = [[JavaParams._from_java(sub_model)
                                   for sub_model in fold_sub_models]
                                  for fold_sub_models in java_stage.subModels()]

        py_stage._resetUid(java_stage.uid())
        return py_stage

 def _from_java(cls, java_stage):
     """
     Given a Java PipelineModel, create and return a Python wrapper of it.
     Used for ML persistence.
     """
     # Load information from java_stage to the instance.
     py_stages = [JavaParams._from_java(s) for s in java_stage.stages()]
     # Create a new instance of this stage.
     py_stage = cls(py_stages)
     py_stage._resetUid(java_stage.uid())
     return py_stage

    def _from_java(cls, java_stage):
        """
        Given a Java CrossValidatorModel, create and return a Python wrapper of it.
        Used for ML persistence.
        """

        # Load information from java_stage to the instance.
        bestModel = JavaParams._from_java(java_stage.bestModel())
        estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
        # Create a new instance of this stage.
        py_stage = cls(bestModel=bestModel)\
            .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator)
        py_stage._resetUid(java_stage.uid())
        return py_stage

    def _to_java(self):
        """
        Transfer this instance to a Java PipelineModel.  Used for ML persistence.

        :return: Java object equivalent to this instance.
        """

        gateway = SparkContext._gateway
        cls = SparkContext._jvm.org.apache.spark.ml.Transformer
        java_stages = gateway.new_array(cls, len(self.stages))
        for idx, stage in enumerate(self.stages):
            java_stages[idx] = stage._to_java()

        _java_obj =\
            JavaParams._new_java_obj("org.apache.spark.ml.PipelineModel", self.uid, java_stages)

        return _java_obj

    def _to_java(self):
        """
        Transfer this instance to a Java CrossValidator. Used for ML persistence.

        :return: Java object equivalent to this instance.
        """

        estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl()

        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid)
        _java_obj.setEstimatorParamMaps(epms)
        _java_obj.setEvaluator(evaluator)
        _java_obj.setEstimator(estimator)
        _java_obj.setSeed(self.getSeed())
        _java_obj.setNumFolds(self.getNumFolds())

        return _java_obj

    def _to_java(self):
        """
        Transfer this instance to a Java CrossValidatorModel. Used for ML persistence.

        :return: Java object equivalent to this instance.
        """

        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidatorModel",
                                             self.uid,
                                             self.bestModel._to_java(),
                                             self.avgMetrics)
        estimator, epms, evaluator = super(CrossValidatorModel, self)._to_java_impl()

        _java_obj.set("evaluator", evaluator)
        _java_obj.set("estimator", estimator)
        _java_obj.set("estimatorParamMaps", epms)
        return _java_obj

    def _to_java(self):
        """
        Transfer this instance to a Java TrainValidationSplit. Used for ML persistence.
        :return: Java object equivalent to this instance.
        """

        estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl()

        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.TrainValidationSplit",
                                             self.uid)
        _java_obj.setEstimatorParamMaps(epms)
        _java_obj.setEvaluator(evaluator)
        _java_obj.setEstimator(estimator)
        _java_obj.setTrainRatio(self.getTrainRatio())
        _java_obj.setSeed(self.getSeed())
        _java_obj.setParallelism(self.getParallelism())

        return _java_obj

    def _to_java(self):
        """
        Transfer this instance to a Java CrossValidatorModel. Used for ML persistence.

        :return: Java object equivalent to this instance.
        """

        sc = SparkContext._active_spark_context
        # TODO: persist average metrics as well
        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidatorModel",
                                             self.uid,
                                             self.bestModel._to_java(),
                                             _py2java(sc, []))
        estimator, epms, evaluator = super(CrossValidatorModel, self)._to_java_impl()

        _java_obj.set("evaluator", evaluator)
        _java_obj.set("estimator", estimator)
        _java_obj.set("estimatorParamMaps", epms)

        if self.subModels is not None:
            java_sub_models = [[sub_model._to_java() for sub_model in fold_sub_models]
                               for fold_sub_models in self.subModels]
            _java_obj.setSubModels(java_sub_models)
        return _java_obj