Cross-Validation Error

In the lecture model selection and training/cross validation/test sets, Sir Andrew Ng explained that cross validation is a better estimator for learning algorithms. Why does the cross validation error is preferred over the test set error?

The test set should only be used once, as a final independent verification of the performance of the model on new data that was not used to train or adjust the model.