In the context of Epidemiology, regularization refers to a set of techniques used to prevent overfitting when building statistical models. Overfitting occurs when a model captures noise or random fluctuations in the data, rather than the underlying pattern. This can lead to poor predictive performance on new, unseen data. Regularization techniques add a penalty to the model's complexity, discouraging the fitting of noise and improving generalizability.
