Overfitting happens when models perform well – with high apparent accuracy – on training data, but that perform poorly on new data. This is often the result of learning from noise or fluctuations in training data. Comparing results to hold-out data reveals the extent of a model’s ability to be useful for generalized predictions, and are good barometers for detecting overfitting.