What are some techniques for model selection in machine learning, and when would you use them?

Model selection techniques are methods to choose the best model among various options for a given dataset and problem. Some popular techniques include Grid Search and Random Search, which systematically explore hyperparameter combinations to find the best performance. Cross-validation helps ensure that the model's performance is consistent across different subsets of the data. Additionally, tools like Bayesian Optimization can be used for more efficient searches. These techniques are particularly useful when you have a multitude of models or hyperparameters to tune, letting you optimize performance and avoid overfitting.