The surrogate model allows us to cheaply approximate the quality of a set of parameters w without running eval(w), and the acquisition function uses this surrogate to choose a new value of w. However, a trade-off must be made: should the acquisition function move w into a region where the surrogate believes an optimal value will be found, or should it explore regions of the space that reveal more about how eval behaves, perhaps discovering even better values?
acquisition functions balance a tradeoff between exploration-by selecting w in the regions where the uncertainty of the surrogate model is high, and exploitation-by querying the regions where the model prediction is high.