We estimate the parameters of 21 simulation models using 9 estimation algorithms to discover which one is better at matching simulations with data. Unfortunately no single algorithm minimizes estimation error for all or even most the estimation tasks; instead algorithm varies for each simulation, and sometimes for each parameter of each simulation. Fortunately it is straightforward to use cross-validation to match to each simulation the best estimation algorithm for it. In terms of confidence intervals the results are more clear: bootstrap generates more precise prediction intervals than either quantiles or Approximate Bayesian Computation.