The F1 score is especially useful in scenarios with imbalanced classes where there's a significant cost associated with false negatives. It balances precision and recall, making it ideal for situations requiring consideration of both false positives and negatives, such as in medical diagnosis or fraud detection. Accuracy may not be reliable in imbalanced datasets as it can be skewed by the majority class.