In toxicology, predicting the toxicological effects of compounds accurately is vital for public health and safety. Cross-validation helps in assessing how well a model will generalize to an independent dataset, ensuring that predictions are reliable before any chemical compound is declared safe or harmful. It provides a check against overfitting, where a model performs well on training data but poorly on unseen data.
