Why is model evaluation important in machine learning, and what are some common evaluation techniques?

Model evaluation is crucial in machine learning because it helps to assess the performance of a model against unseen data, ensuring it generalizes well beyond the training set. Common evaluation techniques include splitting data into training, validation, and test sets, using metrics like accuracy, precision, recall, F1 score, and ROC-AUC for classification tasks, and mean squared error (MSE) and R-squared for regression tasks. Cross-validation is another technique that helps in providing a more reliable estimate of model performance by repeatedly splitting the data and training on different subsets.