Regularization is a common technique to obtain reasonable solutions to ill-posed problems. In Tikhonov regularization, both the data-fitting and the penalty terms are in L2 norm. The L-curve is a plot of the size of the regularized solution versus the size of the corresponding residual for all valid regularization parameters. It is a useful tool for determining a suitable value of the regularization parameter. LASSO replaces the L2 norm by L1 norm for the penality term. The LARS algorithm computes the whole path of the LASSO with a computational complexity in the same magnitude as the ordinary least squares. Thus, the L-curve for LASSO can be obtained efficiently by the LARS-LASSO algorithm. The tuning point of the L-curve is chosen as the value of the regularization parameter. We compare L-curve method with existing methods, including GCV and C_p.