Why do we prefer a larger margin in hard-classification SVM?(Among other reasons, think about the correctness and certainty of data.)

What about in a soft-classification setting -- what's the benefit of a bigger margin (possibly with more error in classifying your known data) vs. a smaller margin (possibly with less error in classifying your known data)?And what's the benefit in the opposite direction (smaller margin) in that case?