docstr: Retrieve the AUC value.
  
  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 train is ``True``, then return the AUC values for the training data.
  :param bool valid: If valid is ``True``, then return the AUC values for the validation data.
  :param bool xval:  If xval is ``True``, then return the AUC values for the cross validation data.
  :returns: The AUC values for this multinomial model.
  
  :examples:
  
  >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
  >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
  >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family = "multinomial"),
  ...                                                  hyper_parameters)
  >>> gs.train(x=[0,1,2,3], y=4, training_frame=iris)
  >>> gs.auc(train=True)
docstr_tok: ["Retrieve","the","AUC","value",".","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","train","is","``","True","``",",","then","return","the","AUC","values","for","the","training","data",".",":","param","bool","valid",":","If","valid","is","``","True","``",",","then","return","the","AUC","values","for","the","validation","data",".",":","param","bool","xval",":","If","xval","is","``","True","``",",","then","return","the","AUC","values","for","the","cross","validation","data",".",":","returns",":","The","AUC","values","for","this","multinomial","model",".",":","examples",":",">",">",">","from","h2o.estimators.glm","import","H2OGeneralizedLinearEstimator",">",">",">","from","h2o.grid.grid_search","import","H2OGridSearch",">",">",">","hyper_parameters","=","{","'alpha","'",":","[","0.01,0.5","]",",","'lambda","'",":","[","1e-5,1e-6","]","}",">",">",">","iris","=","h2o.import_file","(","``","http",":","\/\/h2o-public-test-data.s3.amazonaws.com\/smalldata\/iris\/iris.csv","''",")",">",">",">","gs","=","H2OGridSearch","(","H2OGeneralizedLinearEstimator","(","family","=","``","multinomial","''",")",",","...","hyper_parameters",")",">",">",">","gs.train","(","x=","[","0,1,2,3","]",",","y=4",",","training_frame=iris",")",">",">",">","gs.auc","(","train=True",")"]
code: def auc(self, train=False, valid=False, xval=False):
      """
      Retrieve the AUC value.
  
      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 train is ``True``, then return the AUC values for the training data.
      :param bool valid: If valid is ``True``, then return the AUC values for the validation data.
      :param bool xval:  If xval is ``True``, then return the AUC values for the cross validation data.
      :returns: The AUC values for this multinomial model.
  
      :examples:
  
      >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
      >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family = "multinomial"),
      ...                                                  hyper_parameters)
      >>> gs.train(x=[0,1,2,3], y=4, training_frame=iris)
      >>> gs.auc(train=True)
      """
      return {
          model.model_id: model.auc(train, valid, xval)
          for model in self.models
      }
  
label_desc: Use image node_id 3 for calling the H2OMultinomialGridSearch obj's underlying member method code with example usage: obj.auc(train, valid, xval) and returns: unknown