The best model for predictive analytics depends on factors like the problem, available data, and requirements. Commonly used models include linear regression, logistic regression, decision trees, random forest, gradient boosting methods, support vector machines, neural networks, time series models, and k-nearest neighbors. Each model has strengths and should be evaluated based on the specific task and dataset.