Regularization in the context of support vector machine $($SVM$)$ learning is meant to

lessen the impact that some minor misclassifications have on the cost function

smooth the input data to reduce the chance of overfitting

encourage the model to ignore outliers during training bring all features to a common scale to ensure they have equal weight