Why is it important to keep training and testing data separate?

Correct answer: To evaluate the model’s ability fairly on new data

The training set is used to learn patterns and model parameters, whereas the test set is kept unseen until evaluation. Separating them gives a more trustworthy estimate of how the model will perform on unfamiliar data. If test examples influence training, data leakage can produce an overly optimistic score. Option A is correct; separation does not stop training, erase data, or hide results.