Fisher Scoring v/s Coordinate Descent for MLE in R
The only way to be sure is by benchmarking, but for glm Fisher scoring should be faster than coordinate descent.  Fisher scoring is a special case of Newton Raphson, which has a faster rate of convergence than coordinate descent (Newton-Raphson is quadratically convergent, while coordinate descent is linearly convergent.)  So while the computation of second-derivative information means each step takes more time, it can require many fewer steps than coordinate descent.

For the lasso, the special form of the penalty term makes it a very special case (and in fact absolute value isn't differentiable anyway, though sometimes you can finesse this).  For this special problem, coordinate descent proves to be particularly fast.  There are many other optimization problems where in practice Newton-Raphson is faster.