The mini-batch size is an important parameter in stochastic gradient descent. It plays a role in balancing the noise introduced by small mini-batches and the computational efficiency of processing larger mini-batches. The choice of mini-batch size depends on the specific dataset and computational resources available, and it often requires experimentation to find the optimal value.