Understanding overfitting: an inaccurate meme in Machine Learning

This post was inspired by a recent post by Andrew Gelman, who defined'overfitting' as follows: Overfitting is when you have a complicated model that gives worse predictions, on average, than a simpler model. Preamble There is a lot of confusion among practitioners regarding the concept of overfitting. Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. This statement is of course not true: cross-validation does not prevent your model to overfit and good out-of-sample performance does not guarantee not-overfitted model. What actually people refer to in one aspect of this statement is called overtraining.