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In the context of decision tree models, the parameter that indicates an essential split is the complexity parameter. The complexity parameter is critical in controlling the size and shape of the tree by determining how much pruning should be performed. It helps in balancing between model accuracy and overfitting by penalizing complex models that might memorize the training data.

On the other hand, parameters such as minbucket and minsplit serve different roles; minbucket specifies the minimum number of observations in a terminal node, while minsplit sets the minimum number of observations required to make a split at a node. Maxdepth, which restricts the maximum depth of the tree, influences the size of the tree, but not the essential splits that create the tree's structure.

Thus, the complexity parameter is the most accurate choice for identifying where significant splits occur within the framework of a decision tree model, as it plays a pivotal role in regulating the growth of the model based on its complexity.