Which of the following statements is true?

a. Lasso regularization and ridge regularization are different methods for standardizing variables.

b. Lasso regularization may prevent overfitting of a linear regression model by driving the coefficients of some variables to zero.

c. Regularization is not useful in the training of a regression tree because it has embedded feature selection.

d. Regularization is not helpful in the training of neural networks when there is sufficient data for the neural network.