In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?

Combining cross‑validation with L2 (ridge) regularization helps a parametrized predictor avoid overfitting by first estimating how well the model generalizes on unseen folds and then shrinking large coefficient values. The cross‑validation step gives a realistic error estimate, while the L2 penalty discourages extreme weights that fit noise. In practice you might train a linear regression on house‑price data, use 5‑fold cross‑validation to tune the penalty strength, and then pick the model with the lowest validation error. This approach reduces variance, keeps the model simpler, and typically yields a more reliable evaluation on new data.