class_docstr: K-means
  
  Performs k-means clustering on an H2O dataset.
class_docstr_tok: ["K-means","Performs","k-means","clustering","on","an","H2O","dataset","."]
docstr: Whether to keep the cross-validation models.
  
  Type: ``bool``, defaults to ``True``.
  
  :examples:
  
  >>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv")
  >>> predictors = ["radiation","temperature","wind"]
  >>> train, valid = ozone.split_frame(ratios=[.8], seed=1234)
  >>> ozone_km = H2OKMeansEstimator(keep_cross_validation_models=True,
  ...                               nfolds=5,
  ...                               seed=1234)
  >>> ozone_km.train(x=predictors,
  ...                training_frame=train,
  ...                validation_frame=valid)
  >>> ozone_km.scoring_history()
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code: def keep_cross_validation_models(self):
      """
      Whether to keep the cross-validation models.
  
      Type: ``bool``, defaults to ``True``.
  
      :examples:
  
      >>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv")
      >>> predictors = ["radiation","temperature","wind"]
      >>> train, valid = ozone.split_frame(ratios=[.8], seed=1234)
      >>> ozone_km = H2OKMeansEstimator(keep_cross_validation_models=True,
      ...                               nfolds=5,
      ...                               seed=1234)
      >>> ozone_km.train(x=predictors,
      ...                training_frame=train,
      ...                validation_frame=valid)
      >>> ozone_km.scoring_history()
      """
      return self._parms.get("keep_cross_validation_models")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 8 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.keep_cross_validation_models() and returns: self