Which of the following statements about lasso and ridge regression models is true?

The true statement regarding lasso and ridge regression models is that the number of variables in a lasso regression model can be less than or equal to that in a ridge regression model. This is because lasso regression employs L1 regularization, which has the unique property of performing variable selection by driving some coefficients to zero. This means that lasso can effectively eliminate some predictors from the model entirely, resulting in a more parsimonious model with fewer variables.

On the other hand, ridge regression utilizes L2 regularization, which shrinks the coefficients but does not set any coefficients to zero. Consequently, ridge will retain all the variables in the model, although their influence may be diminished. Therefore, lasso regression can yield a model with fewer variables than ridge regression, or at the very least, it can have an equal number of variables when all coefficients happen to be non-zero.

This distinction in the way these two types of regression handle the inclusion of variables helps clarify why lasso can lead to a model with fewer predictors compared to ridge, supporting the correctness of the statement in question.