You are building a click-through rate prediction model for a digital ads ranking system. You need to choose between simpler and more flexible supervised learning models, and the team wants to understand how bias and variance should influence model selection before the model is used in production scoring.
How would you evaluate and compare candidate models through the lens of bias and variance, and how would that affect your choice of model, regularization, and validation strategy for this prediction task?