From my understanding, when using Lasso regression, you can use GridSearchCV or LassoCV in sklearn to find the optimal alpha, the regularization parameter. Which one is preferred over the other?
You can get the same results with both. LassoCV makes it easier by letting you pass an array of alpha-values to alphas as well as a cross validation parameter directly into the classifier.
To do the same thing with GridSearchCV, you would have to pass it a Lasso classifier a grid of alpha-values (i.e. {'alpha':[.5, 1, 5]}) and the CV parameter.
I would not recommend one over the other though. The only advantage I can see is that you can access results_ as well as many other attributes if you use GridSearchCV. This may be helpful if you want a summary of all the models returned by the alphas you tried. On the other hand, as pointed out by @amiola, LassoCV can take advantage of using pre-computed results in previous steps of the cross-validation process (aka warm-starting), which may result in faster fitting times.