Overfitting is a common problem in predictive modeling where the model learns the noise in the training data instead of the underlying pattern. Cross-validation helps mitigate this by testing the model on different subsets of data, ensuring that the model's performance is not just a result of fitting to the training data but is a true representation of its predictive capability. By using techniques like regularization alongside cross-validation, toxicologists can develop models that generalize well to new data.
