Model comparison is the process of evaluating different statistical or machine learning models to determine which one best fits a given dataset. This involves assessing various performance metrics, such as accuracy, precision, recall, and F1 score, to identify the model that provides the most reliable predictions. In practice, model comparison often includes techniques like cross-validation, where the dataset is split into training and testing subsets. Additionally, tools like AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion) can help quantify the trade-offs between model complexity and goodness of fit, guiding the selection of the optimal model.