How to Interpret Results of lasso() Function for Model Selection

I am currently attempting to determine the most predictive multiple linear regression model to use for a set of data with continuous variables and am trying to figure out the best combination of variables to use in the model as well as the coefficients of those variables.

I have been guided towards Lasso regression as a means to find the most predictive model, though I am a bit unsure in regards to interpreting the results. In MATLAB, I ran my data through the [B,FitInfo] = lasso(X,Y) function. Is the most predictive model the one whose coefficients are B(:,FitInfo.IndexMinMSE)?

Once I have found the model that tells me which combination of coefficients are most predictive, do I use the coefficient results provided by the lasso function to use for the regression, or do I re-run a multiple linear regression using the remaining variables and use those coefficients?

Thanks for the help!