The lower it gets the smaller the steps done at each boosting iteration.
It can get absurdly slow and you might get diminishing returns quickly when using a small eta. Ideally the eta should be small enough but not too small, in order to allow an acceptable convergence speed while maintaining a good enough performance.
Note that sometimes a too small learning rate prevents learning properly.
Cross-validation can help finding an estimation of the appropriate number of rounds automatically.