# What's the typical range of possible values for the shrinkage parameter in penalized regression?

In lasso or ridge regression, one has to specify a shrinkage parameter, often called by $\lambda$ or $\alpha$. This value is often chosen via cross validation by checking a bunch of different values on training data and seeing which yields the best e.g. $R^2$ on test data. What is the range of values one should check? Is it $(0,1)$?

You don't really need to bother. In most packages (like glmnet) if you do not specify $\lambda$, the software package generates its own sequence (which is often recommended). The reason I stress this answer is that during the running of the LASSO the solver generates a sequence of $\lambda$, so while it may counterintuitive providing a single $\lambda$ value may actually slow the solver down considerably (When you provide an exact parameter the solver resorts to solving a semi definite program which can be slow for reasonably 'simple' cases.)
As for the exact value of $\lambda$ you can potentially chose whatever you want from $[0,\infty[$. Note that if your $\lambda$ value is too large the penalty will be too large and hence none of the coefficients can be non-zero. If the penalty is too small you will overfit the model and this will not be the best cross validated solution