Cost-Sensitive Decision Trees are a variation of decision trees that incorporate the costs associated with different types of classification errors, making them particularly useful for applications where the consequences of false positives and false negatives are significantly different. By integrating cost considerations directly into the model-building process, these trees aim to minimize the total expected cost rather than simply maximizing accuracy.