Explanation
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It