By fitting complex functions, we might be able to perfectly match the training data with zero loss. In this video, we learn how to separate the data into training and evaluation data. If the training loss is better than the evaluation loss, our function is too complicated, and we are over-fitting to the training data. We should use cross validation to make sure that our model works just as well for unseen data.
