A memorization machine is a theoretical classification model that perfectly memorizes the training dataset, thereby always achieving an empirical error of $0$. However, this extreme flexibility means it completely fails to generalize to new, unseen data, performing no better than random guessing on the underlying population. Because this model class is excessively flexible, it serves as a primary counterexample demonstrating that uniform convergence cannot hold for all possible model classes.