docstr: Fits the wrapped grid search and calls draw().
  
  Parameters
  ----------
  X : ndarray or DataFrame of shape n x m
      A matrix of n instances with m features
  
  y : ndarray or Series of length n
      An array or series of target or class values
  
  kwargs: dict
      Keyword arguments passed to the drawing functionality or to the
      Scikit-Learn API. See visualizer specific details for how to use
      the kwargs to modify the visualization or fitting process.
  
  Returns
  -------
  self : visualizer
      The fit method must always return self to support pipelines.
code: def fit(self, X, y=None, **kwargs):
      """
      Fits the wrapped grid search and calls draw().
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n
          An array or series of target or class values
  
      kwargs: dict
          Keyword arguments passed to the drawing functionality or to the
          Scikit-Learn API. See visualizer specific details for how to use
          the kwargs to modify the visualization or fitting process.
  
      Returns
      -------
      self : visualizer
          The fit method must always return self to support pipelines.
      """
      self.estimator.fit(X, y)
      self.draw()
      return self
  
file_path: yellowbrick/yellowbrick/gridsearch/base.py
imports: import numpy
  from ..utils import is_gridsearch
  from ..base import ModelVisualizer
  from ..exceptions import YellowbrickTypeError, YellowbrickKeyError, YellowbrickValueError
label_desc: Use image node_id 3 for calling the GridSearchVisualizer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self