What is the intuition behind decreasing the slope when using regularization?

Using regularization and shrinking the parameters, we reduce the sample variance of the estimates, and reduce the tendency to fit the random noise. The fitting to the noise is something that we wish to reduce. We can't increase the slope as we wan't to reduce overfitting.

L2 doesn’t necessarily reduce the number of features, but rather reduces the magnitude/impact that each features has on the model by reducing the coefficient value.

Shrinking can lead to positive effect when we have overestimated and negative when we have underestimated. But we are not shrinking everyone equally ,we are shifting with a factor that is larger if the estimate is larger away from zero.

Shrinking all the slopes towards zero will make some of them more accurate and some of them less accurate, but you can see how it would make them collectively more accurate.

Please refer this article on L1 and L2 regularization :- https://towardsdatascience.com/intuitions-on-l1-and-l2-regularisation-235f2db4c261

L1 called as Lasso and L2 called as Ridge essentially reduces the learning process of the gradient descent ( loss reduction) in an attempt to reduce overfitting. As far as I know only L1 has the impact of reducing the coefficents of lesser effective features and not L2.