variables selection in regression models
Lasso stands for ´least absolute shrinkage and selection operator´. It has a penalty that is the absolute value and makes a lot of variables converge to cero. There is a ton of blogs that explain really well Lasso on the internet, have a look!

Elastic Net is a combination of Ridge and Lasso. So it will also reduce the variables a lot. 

Ridge is a quadratic penalty so the convergence is mellower. There are also some posts about why this happens Why will ridge regression not shrink some coefficients to zero like lasso?


In Multiple Linear regression, there is no penalty, so coefficients have not a special reason to be zero. They will only be cero when there is no relation with the target variable. Imagine that you add a feature that is just random noise. You will end up with a cero coefficient for your linear model.

This should give you an intuition of why Lasso shrinks most of the features, Enet a little bit less, Ridge not so many and Linear Regression only some or none.