When you have underfitting or overfitting issues in a statistical model, you can use the regularization technique to resolve it. Regularization techniques like LASSO help penalize some model parameters if they are likely to lead to overfitting.

If the interviewer follows up with a question about other methods that can be used to avoid overfitting, you can mention cross-validation techniques such as k-folds cross-validation.

Another approach is to keep the model simple by taking into account fewer variables and parameters. Doing this helps remove some of the noise in the training data.