Soft clustering is a method in which data points can belong to multiple clusters with varying degrees of membership, typically represented by probabilities or fuzzy values. Unlike hard clustering, which assigns each data point to a single cluster, soft clustering provides a more nuanced understanding of data structure, particularly useful in complex datasets where boundaries between clusters are not well-defined.