Cost-Sensitive SVM is a variation of the Support Vector Machine that incorporates different misclassification costs for different classes, making it particularly useful in imbalanced datasets where one class is more critical to predict correctly. By assigning higher penalties to misclassifying instances of the minority class, it helps to improve the classifier's performance on the more important or rare class without significantly degrading performance on the majority class.