docstr: Get the between cluster sum of squares.
  
  If all are ``False`` (default), then return the training metric value.
  If more than one option is set to ``True``, then return a dictionary of metrics where
  the keys are "train", "valid", and "xval".
  
  :param bool train: If ``True``, then return the between cluster sum of squares value for the training data.
  :param bool valid: If ``True``, then return the between cluster sum of squares value for the validation data.
  :param bool xval: If ``True``, then return the between cluster sum of squares value for each of the
      cross-validated splits.
  :returns: the between cluster sum of squares values for the specified key(s).
  
  :examples:
  
  >>> from h2o.estimators import H2OKMeansEstimator
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
  >>> hyper_parameters = {'k': [2,3,4], 'init': "random"}
  >>> gs = H2OGridSearch(H2OKMeansEstimator(), hyper_parameters)
  >>> gs.train(x=list(range(4)), training_frame=iris)
  >>> gs.betweenss(train=True)
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code: def betweenss(self, train=False, valid=False, xval=False):
      """
      Get the between cluster sum of squares.
  
      If all are ``False`` (default), then return the training metric value.
      If more than one option is set to ``True``, then return a dictionary of metrics where
      the keys are "train", "valid", and "xval".
  
      :param bool train: If ``True``, then return the between cluster sum of squares value for the training data.
      :param bool valid: If ``True``, then return the between cluster sum of squares value for the validation data.
      :param bool xval: If ``True``, then return the between cluster sum of squares value for each of the
          cross-validated splits.
      :returns: the between cluster sum of squares values for the specified key(s).
  
      :examples:
  
      >>> from h2o.estimators import H2OKMeansEstimator
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
      >>> hyper_parameters = {'k': [2,3,4], 'init': "random"}
      >>> gs = H2OGridSearch(H2OKMeansEstimator(), hyper_parameters)
      >>> gs.train(x=list(range(4)), training_frame=iris)
      >>> gs.betweenss(train=True)
      """
      return {
          model.model_id: model.betweenss(train, valid, xval)
          for model in self.models
      }
  
label_desc: Use image node_id 3 for calling the H2OClusteringGridSearch obj's underlying member method code with example usage: obj.betweenss(train, valid, xval) and returns: unknown