You mean theoretically? These “shrinkage” methods have two common uses
- they can be used to shrink (often to 0) parameters estimates in a large parameter model, e.g. you have 100 candidate parameters and when you fit the model many of them look significant but you wanted a parsimonious model, so you would use things like elastic net etc.
- when you don’t have that much data but you want to stabilise some of parameter estimates to make them more moderate; there are some studies that shows some of these methods are equivalent to adding data points to your dataset.
This is a pretty rough overview, but the only times I recall applying these methods is in situation 1. where I have lots of candidate explanatory variables but I wanted to reasonable check on the parameters to estimate.
Also using these method can be tricky sometimes as you often have to tune some hyperparameters as well. These parameters can serve many purposes, but some important one control how much large parameter estimates impact your final estimate, and to find the “optimal” hyperparameter you would often use cross-validation technique which are computationally intensive, so I didn’t find them that useful for really large datasets. Also there are other ways to help select variables in large-data settings, so you don’t have to use these shrinkage methods; but they are still useful to understand and to benchmark your results with.