RF can be tune by different parameters.
The most important parameter is the number of variables randomly sampled. The author recommend different values for regression and classification. But it is worth to tune by your own problem and data set.
The size of terminal nodes of trees may also affect the performance. Similar to above, there are default values recommended by the author, but we can tune it.
The number of trees may be tuned if needed, but RF is seldom overfitted. So this may not affect too much if the number of trees is large enough.
Well, different model has different advantages and different strong application which they are suitable. This depends on the characteristics of your data set, some other special models are likely to better than RF in some specific topics. But RF is easy to tune, and it is usually reasonably good model, generally speaking, even by default setting.