Inter-class variance is a statistical measure used to quantify the degree of separation between different classes in a dataset, often used in classification tasks to improve model accuracy. Maximizing inter-class variance enhances the distinctiveness of classes, thereby aiding in better decision boundaries for machine learning algorithms.