Refers to the disparity in predictions generated by a machine learning model when trained on diverse datasets, highlighting its sensitivity to fluctuations in data. Variance quantifies the extent to which model outputs deviate across different training instances, reflecting its susceptibility to changes in input variables or dataset compositions. High variance implies that the model is overly influenced by fluctuations in training data, potentially leading to overfitting and reduced generalization performance. Managing variance is crucial in ensuring model robustness and reliability across various datasets, enhancing its ability to generalize well to unseen data and improve overall predictive accuracy.