But what does it mean to "fit the test set"? The answer is simple: whichever model assigns a higher probability to the test set-meaning it more accurately predicts the test set-is a better model. Given two probabilistic models, the better model is the one that has a tighter fit to the test data or that better predicts the details of the test data, and hence will assign a higher probability to the test data.