Your question is Handling Overfitting in Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're training a supervised learning model and notice it performs much better on the training set than on validation data. You want to improve generalization without throwing away useful signal.
How would you handle overfitting in a model?