docstr: Return the coordinates of the ROC curve for a given set of data, as a two-tuple containing the false positive
  rates as a list and true positive rates as a list.
  
  If all are ``False`` (default), then return  the training data.
  If more than one ROC curve is requested, the data is returned as a dictionary of two-tuples.
  
  :param bool train: If train is ``True``, then return the ROC coordinates for the training data.
  :param bool valid: If valid is ``True``, then return the ROC coordinates for the validation data.
  :param bool xval: If xval is ``True``, then return the ROC coordinates for the cross validation data.
  :returns: the true cooridinates of the roc curve.
  
  :examples:
  
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  >>> training_data = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
  >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
  >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
  ...                                                  hyper_parameters)
  >>> gs.train(x=[3, 4-11],
  ...          y=3,
  ...          training_frame=training_data)
  >>> gs.roc(train=True)
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code: def roc(self, train=False, valid=False, xval=False):
      """
      Return the coordinates of the ROC curve for a given set of data, as a two-tuple containing the false positive
      rates as a list and true positive rates as a list.
  
      If all are ``False`` (default), then return  the training data.
      If more than one ROC curve is requested, the data is returned as a dictionary of two-tuples.
  
      :param bool train: If train is ``True``, then return the ROC coordinates for the training data.
      :param bool valid: If valid is ``True``, then return the ROC coordinates for the validation data.
      :param bool xval: If xval is ``True``, then return the ROC coordinates for the cross validation data.
      :returns: the true cooridinates of the roc curve.
  
      :examples:
  
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
      >>> training_data = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
      ...                                                  hyper_parameters)
      >>> gs.train(x=[3, 4-11],
      ...          y=3,
      ...          training_frame=training_data)
      >>> gs.roc(train=True)
      """
      return {
          model.model_id: model.roc(train, valid, xval)
          for model in self.models
      }
  
label_desc: Use image node_id 20 for calling the H2OBinomialGridSearch obj's underlying member method code with example usage: obj.roc(train, valid, xval) and returns: unknown