Answer / Shikha Saxena

Model selection in machine learning refers to the process of choosing the most appropriate model among several candidates for a given dataset and problem. The goal is to select a model that generalizes well to unseen data, balancing between bias (underfitting) and variance (overfitting). Model selection can be done using techniques such as cross-validation, AIC (Akaike Information Criteria), BIC (Bayesian Information Criteria), or grid search.

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