Ridge regression is a key regularization technique that penalizes the L2-norm of the coefficient values in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter alpha that controls the amount of regularization. Cross-validation is typically used to select the best alpha from a set of candidate values. However, efficient and appropriate selection of alpha can be challenging, particularly in data-driven research where large amounts of data are...