Misleading modelling: overfitting, cross-validation, and the bias-variance trade-off

This idea of building generalizable models is the motivation behind splitting your dataset into a training set (on which models can be trained) and a test set (which is held out until the very end of your analysis, and provides an accurate measure of model performance).