It's not immediately what exactly you're doing when fitting a model and what you goal is. I'll answer as best I can with the information provided.
GLMNET has two tuning parameters. A sequence of $\lambda$s is generated internally; the user supplies a value of $\alpha$.
The stated question is how to choose a GLMNET model that has 10-15 predictors. The number of nonzero predictors is tracked by the software. So for the supplied value of $\alpha$, just pick the solution corresponding to a $\lambda$ value that provides the desired number of predictors. On the assumption that the supplied value of $\alpha$ is "known," you're done. If you're uncertain about alpha (perhaps due to a desire to also account for collinearity), you'll have to tune over $\alpha$ and compare alternative models according to some appropriate out-of-sample metric in the usual way.
Also of interest may be my answer here. It's worth noting that this answer is highly controversial among several highly-ranked CV contributors, and I'm not certain about how to correctly approach the issue.