Pls help with the following question:

Which of the following statements are true? Check all that apply. To find a regularization parameter, lambda in ridge regression, you need to pick one with lowest training error. To find a regularization parameter, lambda in ridge regression, you need to pick one with lowest validation error. Introducing regularization to the model always results in equal or better performance on examples not in the training set. Adding many new features to the model makes it more likely to overfit the training set. Using a too large value of lambda can cause your hypothesis to underfit the data. Using a too large value of lambda can cause your hypothesis to overfit the data