# Should a training set be used for grid search with cross validation?

I'm looking at an example of using grid search in sklearn, and noticed that after doing train-test splits, the author performs grid search using only the training data.

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0)
...

clf = GridSearchCV(SVC(), tuned_parameters, cv=5,
scoring='%s_macro' % score)
clf.fit(X_train, y_train)


sklearn's GridSearchCV performs k-fold cross validation as part of the grid search. Given this, wouldn't we want to utilize the entire data set, since CV performs its own validation splits? Is there a concept I'm not understanding here?