Double descent is the phenomenon in which the test error of a model (particularly deep neural networks) first decreases, then increases, then decreases again, as a function of its "size" (the number of parameters), or of the number of training epochs. This is surprising in light of the bias-variance trade-off associated with most "classical" ML models.