Model evaluation and validation are essential components of the machine learning pipeline, ensuring that a model performs well and generalizes effectively to unseen data. Evaluation involves assessing the model’s performance using various metrics, such as accuracy, precision, recall, F1 score, and ROC-AUC, depending on the nature of the problem (classification or regression). Validation, on the other hand, involves techniques like cross-validation, where the data is split into training and validation subsets multiple times to assess the model’s stability and generalizability. This process helps to prevent overfitting, where a model performs well on training data but poorly on new data. By employing these evaluation and validation methods, practitioners can gauge a model’s reliability and make informed decisions about its deployment and further improvements.