I am currently studying about lasso regression.

**lasso regression** - In statistics and machine learning, lasso (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.

Here Lambda is the tuning parameter.I want to know why it is necessary that we have to use only cross-validation approach to finding the lambda why we can not use other approaches like AIC, BIC