Elastic net regression in orange
Finally I found the answer. In elastic net the cost function is written as
$J(\theta)=MSE + r\alpha\sum_{i=1}^n \vert\theta_i \vert + \frac {1-r}{2}\alpha\sum_{1=1}^n \theta_i ^2 $. Here $r$ is the ratio, $\alpha$ is the hyperparameter and $n$ is the number of features. The ratio slider controls $r$ and $\alpha$ slider controls the value of hyperparameter.
If the ratio $r=0$, then Elastic net regression is equal to ridge regression. For $r=1$, the Elastic Net Regression is equal to Lasso regression. I hope this perfectly answers the question I posed. I would like to have comments from followers of this post.