Regularization is a way to prevent overfitting by adding a penalty function to the loss function of a machine learning algorithm. For example in the case of a logistic regression, one might add a L1 or L2 regularization which penalizes the loss function when coefficients are either driven to zero or to smaller values to prevent the model from becoming overly complex.