Stochastic Subgradient Methods extend the subgradient method, enabling it to handle optimization problems with nondifferentiable and non-deterministic functions by using stochastic approximation. They offer a way to efficiently optimize non-differentiable functions when the exact gradient isn't available, making them suitable for large-scale and machine learning applications where data points might come in a stream, or the exact gradient is computationally expensive to obtain.