One of the most fascinating findings in the statistical/machine learning literature of the last 10 years is the phenomenon called double descent. For some reason, extremely over-parameterized models, where the number of parameters to estimate is 10-1000 times more than the number of observations, sometimes win competitions, prove to be the best models.