Divides the training set into two parts - a training set and a validation set (also known as the “holdout set”). A model is made to fit the training set, and then the validation set is used to test the accuracy of the model because the model is blind to it. This gives an estimate of the test error of the model and can be useful in determining the best model from a series of models using different hyperparameters.