Your question is Bias-Variance Tradeoff in Practice. 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 are comparing several supervised learning models and want a principled way to explain why some underfit while others overfit.
Can you define the bias-variance tradeoff and explain how it impacts model performance?