A. Regularization is a technique that avoids overfitting by penalizing large coefficients in the model. In SVM for Regression, the regularization parameter determines the trade-off between achieving a low error on the training data and minimizing the complexity of the regression model. A higher value of the regularization parameter increases the penalty for large coefficients, which helps to prevent the model from fitting the noise in the training data.