title: How to apply StandardScaler in Pipeline in scikit-learn (sklearn)?
body: <p>In the example below,</p>
  
  <pre><code>pipe = Pipeline([
          ('scale', StandardScaler()),
          ('reduce_dims', PCA(n_components=4)),
          ('clf', SVC(kernel = 'linear', C = 1))])
  
  param_grid = dict(reduce_dims__n_components=[4,6,8],
                    clf__C=np.logspace(-4, 1, 6),
                    clf__kernel=['rbf','linear'])
  
  grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=1, verbose=2)
  grid.fit(X_train, y_train)
  print(grid.score(X_test, y_test))
  </code></pre>
  
  <p>I am using <code>StandardScaler()</code>, is this the correct way to apply it to test set as well?</p>