Your question is Bias Variance and Regularization. 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 comparing supervised learning models of different complexity and want a principled way to explain why some underfit while others overfit.
Explain the bias-variance tradeoff mathematically and describe how specific regularization techniques (L1 vs. L2) affect model weights.