Your question is Machine Learning Model Optimization. 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 several candidate models for a supervised learning task and trying to improve generalization without overfitting. You want to explain how you optimize models in practice, from selecting the right objective to tuning complexity and validating results.
What is your experience with machine learning model optimization techniques?